Plasma Proteomics Analysis for Screening Anlotinib Responders in Non-small Cell Lung Cancer

Lü, Jun; Zhang, Wei; Zhang, Lele; Lou, Yu‐Qing; Gu, Ping; Nei, Wei; Qian, Jie; Xu, Jun; Zhao, Xiaodong; Zhong, Hua; Wang, Huimin; Han, Baohui

doi:10.21203/rs.3.rs-26596/v1

Cited by 1 publication

(2 citation statements)

References 23 publications

(46 reference statements)

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“…We identified 740 proteins in total using TMT-based quantitative proteomics on an Orbitrap Fusion Lumos MS instrument, which is on par with recent plasma proteomics publications [83][84][85][86][87][88][89][90]. However, with substantial increases in high pH reversed-phase fractionation in the sample processing, higher numbers of plasma protein identifications could have been identified [91].…”

Section: Discussionmentioning

confidence: 80%

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Why Inclusion Matters for Alzheimer’s Disease Biomarker Discovery in Plasma

Khan

Desaire

López

et al. 2021

JAD

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Background: African American/Black adults have a disproportionate incidence of Alzheimer’s disease (AD) and are underrepresented in biomarker discovery efforts. Objective: This study aimed to identify potential diagnostic biomarkers for AD using a combination of proteomics and machine learning approaches in a cohort that included African American/Black adults. Methods: We conducted a discovery-based plasma proteomics study on plasma samples (N = 113) obtained from clinically diagnosed AD and cognitively normal adults that were self-reported African American/Black or non-Hispanic White. Sets of differentially-expressed proteins were then classified using a support vector machine (SVM) to identify biomarker candidates. Results: In total, 740 proteins were identified of which, 25 differentially-expressed proteins in AD came from comparisons within a single racial and ethnic background group. Six proteins were differentially-expressed in AD regardless of racial and ethnic background. Supervised classification by SVM yielded an area under the curve (AUC) of 0.91 and accuracy of 86%for differentiating AD in samples from non-Hispanic White adults when trained with differentially-expressed proteins unique to that group. However, the same model yielded an AUC of 0.49 and accuracy of 47%for differentiating AD in samples from African American/Black adults. Other covariates such as age, APOE4 status, sex, and years of education were found to improve the model mostly in the samples from non-Hispanic White adults for classifying AD. Conclusion: These results demonstrate the importance of study designs in AD biomarker discovery, which must include diverse racial and ethnic groups such as African American/Black adults to develop effective biomarkers.

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Section: Discussionmentioning

confidence: 80%

“…This choice is potentially critical for facilitating biomarker discovery by focusing on robust protein changes [96]. Despite the use of MS 3 , which requires a higher duty cycle, we were able to identify similar numbers of proteins compared to recent publications using MS/MS approaches [83][84][85][86][87][88][89][90].…”

Section: Study Strengths and Limitationsmentioning

confidence: 72%